OpenRaiser/Intern-Atlas huggingface dataset:
Achieving Maximum-likelihood Detection Performance with Square-order Complexity in Large Quasi-Symmetric MIMO Systems
Venue: arXiv cs.IT 2023
Year: 2023

We focus on the signal detection for large quasi-symmetric (LQS) multiple-input multiple-output (MIMO) systems, where the numbers of both service (M) and user (N) antennas are large and N/M tends to 1. It is challenging to achieve maximum-likelihood detection (MLD) performance with square-order complexity due to the ill-conditioned channel matrix. In the emerging MIMO paradigm termed with an extremely large aperture array, the channel matrix can be more ill-conditioned due to spatial non-stationarity. In this paper, projected-Jacobi (PJ) is proposed for signal detection in (non-) stationary LQS-MIMO systems. It is theoretically and empirically demonstrated that PJ can achieve MLD performance, even when N/M = 1. Moreover, PJ has square-order complexity of N and supports parallel computation. The main idea of PJ is to add a projection step and to set a (quasi-) orthogonal initialization for the classical Jacobi iteration. Moreover, the symbol error rate (SER) of PJ is mathematically derived and it is tight to the simulation results.
Discourse over Discourse: The Need for an Expanded Pragmatic Focus in Conversational AI
Venue: arXiv cs.CL 2023
Year: 2023

The summarization of conversation, that is, discourse over discourse, elevates pragmatic considerations as a pervasive limitation of both summarization and other applications of contemporary conversational AI. Building on impressive progress in both semantics and syntax, pragmatics concerns meaning in the practical sense. In this paper, we discuss several challenges in both summarization of conversations and other conversational AI applications, drawing on relevant theoretical work. We illustrate the importance of pragmatics with so-called star sentences, syntactically acceptable propositions that are pragmatically inappropriate in conversation or its summary. Because the baseline for quality of AI is indistinguishability from human behavior, we draw heavily on the psycho-linguistics literature, and label our complaints as "Turing Test Triggers" (TTTs). We discuss implications for the design and evaluation of conversation summarization methods and conversational AI applications like voice assistants and chatbots
DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Although purely transformer-based architectures showed promising performance in many computer vision tasks, many hybrid models consisting of CNN and transformer blocks are introduced to fit more specialized tasks. Nevertheless, despite the performance gain of both pure and hybrid transformer-based architectures compared to CNNs in medical imaging segmentation, their high training cost and complexity make it challenging to use them in real scenarios. In this work, we propose simple architectures based on purely convolutional layers, and show that by just taking advantage of the attention map visualizations obtained from a self-supervised pretrained vision transformer network (e.g., DINO) one can outperform complex transformer-based networks with much less computation costs. The proposed architecture is composed of two encoder branches with the original image as input in one branch and the attention map visualizations of the same image from multiple self-attention heads from a pre-trained DINO model (as multiple channels) in the other branch. The results of our experiments on two publicly available medical imaging datasets show that the proposed pipeline outperforms U-Net and the state-of-the-art medical image segmentation models.
SCOPE: Structural Continuity Preservation for Medical Image Segmentation
Venue: arXiv cs.CV 2023
Year: 2023

Although the preservation of shape continuity and physiological anatomy is a natural assumption in the segmentation of medical images, it is often neglected by deep learning methods that mostly aim for the statistical modeling of input data as pixels rather than interconnected structures. In biological structures, however, organs are not separate entities; for example, in reality, a severed vessel is an indication of an underlying problem, but traditional segmentation models are not designed to strictly enforce the continuity of anatomy, potentially leading to inaccurate medical diagnoses. To address this issue, we propose a graph-based approach that enforces the continuity and connectivity of anatomical topology in medical images. Our method encodes the continuity of shapes as a graph constraint, ensuring that the network's predictions maintain this continuity. We evaluate our method on two public benchmarks on retinal vessel segmentation, showing significant improvements in connectivity metrics compared to traditional methods while getting better or on-par performance on segmentation metrics.
An Adaptive Channel Reservation MAC Protocol Based on Forwarding Traffic of Key Nodes
Venue: arXiv cs.NI 2023
Year: 2023

Ad Hoc networks with multi-hop topology are widely used in military and civilian applications. One challenge for Ad Hoc networks is to design efficient Media Access Control (MAC) protocols to ensure the quality of service (QoS). In Ad Hoc networks, there is a kind of node called key node, which undertakes more forwarding traffic than other surrounding nodes. The number of neighbor nodes around key nodes is often large, and the surrounding channel environment and interference are often more complex. Thus, the key nodes can hardly get enough channel access opportunities, resulting in poor end-to-end performance. Therefore, we propose an adaptive channel reservation MAC protocol based on forwarding traffic of key nodes, which is aimed at alleviating the congestion for key nodes. Nodes initiate reservations for future transmission time according to the buffer status before sending packets and then calculate the Weight of Reservation Ability (WRA). The node adaptively adjusts its reservation opportunity by comparing the WRA with neighbor nodes, thus improving the channel access efficiency and ensuring the transmission opportunity of key nodes. Extensive simulation confirms that our proposed FTKN-CRM provides significant improvements in end-to-end performance over the IEEE 802.11ax protocol and other reservation access protocols.
Ultrasound Visualization using VTK
Venue: arXiv cs.GR 2023
Year: 2023

This project developed a web application using VTK for ultrasound visualization. The images were enhanced using median and Gaussian filters, and two algorithms were utilized for data visualization: isosurface extraction and Delaunay triangulation. Results showed that both algorithms were effective at reducing Gaussian noise and high-frequency noise, such as speckle noise, which is common in ultrasound. The web application allows users to select MHA files, adjust the marching cubes threshold, and switch between the two algorithms in runtime. This project demonstrates the benefits of ultrasound visualization in medical applications and the utility of using VTK to achieve high-quality visualizations. The web application provides healthcare professionals with a user-friendly platform to interpret ultrasound data, leading to better diagnosis and treatment decisions
Deep Intellectual Property Protection: A Survey
Venue: arXiv cs.AI 2023
Year: 2023

Deep Neural Networks (DNNs), from AlexNet to ResNet to ChatGPT, have made revolutionary progress in recent years, and are widely used in various fields. The high performance of DNNs requires a huge amount of high-quality data, expensive computing hardware, and excellent DNN architectures that are costly to obtain. Therefore, trained DNNs are becoming valuable assets and must be considered the Intellectual Property (IP) of the legitimate owner who created them, in order to protect trained DNN models from illegal reproduction, stealing, redistribution, or abuse. Although being a new emerging and interdisciplinary field, numerous DNN model IP protection methods have been proposed. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of two mainstream DNN IP protection methods: deep watermarking and deep fingerprinting, with a proposed taxonomy. More than 190 research contributions are included in this survey, covering many aspects of Deep IP Protection: problem definition, main threats and challenges, merits and demerits of deep watermarking and deep fingerprinting methods, evaluation metrics, and performance discussion. We finish the survey by identifying promising directions for future research.
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation
Venue: arXiv cs.CV 2023
Year: 2023

In this paper, we trialled different methods of data preparation for Convolutional Neural Network (CNN) training and semantic segmentation of land use land cover (LULC) features within aerial photography over the Wet Tropics and Atherton Tablelands, Queensland, Australia. This was conducted through trialling and ranking various training patch selection sampling strategies, patch and batch sizes and data augmentations and scaling. We also compared model accuracy through producing the LULC classification using a single pass of a grid of patches and averaging multiple grid passes and three rotated version of each patch. Our results showed: a stratified random sampling approach for producing training patches improved the accuracy of classes with a smaller area while having minimal effect on larger classes; a smaller number of larger patches compared to a larger number of smaller patches improves model accuracy; applying data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced and more accurate and aesthetic result. Combining the findings from the trials, we fully trained five models on the 2018 training image and applied the model to the 2015 test image with the output LULC classifications achieving an average kappa of 0.84 user accuracy of 0.81 and producer accuracy of 0.87. This study has demonstrated the importance of data pre-processing for developing a generalised deep-learning model for LULC classification which can be applied to a different date and sensor. Future research using CNN and earth observation data should implement the findings of this study to increase LULC model accuracy and transferability.
DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration
Venue: arXiv cs.DC 2023
Year: 2023

Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior researches adopt the control-flow paradigm to orchestrate a serverless workflow. However, the control-flow paradigm inherently results in long response latency, due to the heavy data persistence overhead, sequential resource usage, and late function triggering. Our investigation shows that the data-flow paradigm has the potential to resolve the above problems, with careful design and optimization. We propose DataFlower, a scheme that achieves the data-flow paradigm for serverless workflows. In DataFlower, a container is abstracted to be a function logic unit and a data logic unit. The function logic unit runs the functions, and the data logic unit handles the data transmission asynchronously. Moreover, a host-container collaborative communication mechanism is used to support efficient data transfer. Our experimental results show that compared to state-of-the-art serverless designs, DataFlower reduces the 99\%-ile latency of the benchmarks by up to 35.4\%, and improves the peak throughput by up to 3.8X.
Hybrid Key Authentication Scheme for Privacy over Adhoc Communication
Venue: arXiv cs.CR 2023
Year: 2023

Since communication signals are publicly exposed while they transmit across space, Ad Hoc Networks (MANETs) are where secured communication is most crucial. Unfortunately, these systems are more open to intrusions that range from passive listening to aggressive spying. A Hybrid Team centric Re-Key Control Framework (HT-RCF) suggests that this research examines private group communication in Adhoc environments. Each group selects a Group Manager to oversee the group's members choose the group manager, and the suggested HT-RCF uses the Improved Hybrid Power-Aware Decentralized (I-HPAD) mechanism. The Key Distribution Center (KDC) generates the keys and distributes them to the group managers (GMs) using the base algorithm Rivest Shamir Adleman (RSA). The key agreement technique is investigated for safe user-user communication. Threats that aim to exploit a node are recognized and stopped using regular transmissions. The rekeying procedure is started every time a node enters and exits the network. The research findings demonstrate that the suggested approach outperforms the currently used Cluster-based Group Key Management in terms of power use, privacy level, storage use, and processing time.
Trust Aware Privacy Preserving Routing Protocol for Wireless Adhoc Network
Venue: arXiv cs.CR 2023
Year: 2023

Wireless Ad-Hoc Networks are especially helpful and quite well for essential circumstances such as defense, public safety, and disaster recovery. MANETs require communication privacy and security, notably in core routing protocols, when functioning in hostile or suspicious environments. The Trust Aware Privacy-Preserving Protocol (TAP3) is a mechanism for supporting the origin in proactively selecting a trust-able target and doing privacy-preserving route verification. We suggest TAP3 using the fellow recommendation model for MANETs in this work. Nodes use their features to discover their fellow node and use the trust to create strong connections with the random node via a multi-hop trusting chain by identifying the secure location. The verification duties are then spread among the nodes and validate the log updates without exposing the nodes' details. Unlike previous models that uncover node vulnerabilities or misconduct after an attack, TAP3 may guarantee the origin node to prevent data from being transferred through malicious nodes from the beginning and do verification without needing a third party. Our results show that this approach can locate problematic nodes with minimal overhead than the conventional routing protocol.
From Explicit Communication to Tacit Cooperation:A Novel Paradigm for Cooperative MARL
Venue: arXiv cs.MA 2023
Year: 2023

Centralized training with decentralized execution (CTDE) is a widely-used learning paradigm that has achieved significant success in complex tasks. However, partial observability issues and the absence of effectively shared signals between agents often limit its effectiveness in fostering cooperation. While communication can address this challenge, it simultaneously reduces the algorithm's practicality. Drawing inspiration from human team cooperative learning, we propose a novel paradigm that facilitates a gradual shift from explicit communication to tacit cooperation. In the initial training stage, we promote cooperation by sharing relevant information among agents and concurrently reconstructing this information using each agent's local trajectory. We then combine the explicitly communicated information with the reconstructed information to obtain mixed information. Throughout the training process, we progressively reduce the proportion of explicitly communicated information, facilitating a seamless transition to fully decentralized execution without communication. Experimental results in various scenarios demonstrate that the performance of our method without communication can approaches or even surpasses that of QMIX and communication-based methods.
Graph Neural Networks on Factor Graphs for Robust, Fast, and Scalable Linear State Estimation with PMUs
Venue: arXiv cs.LG 2023
Year: 2023

As phasor measurement units (PMUs) become more widely used in transmission power systems, a fast state estimation (SE) algorithm that can take advantage of their high sample rates is needed. To accomplish this, we present a method that uses graph neural networks (GNNs) to learn complex bus voltage estimates from PMU voltage and current measurements. We propose an original implementation of GNNs over the power system's factor graph to simplify the integration of various types and quantities of measurements on power system buses and branches. Furthermore, we augment the factor graph to improve the robustness of GNN predictions. This model is highly efficient and scalable, as its computational complexity is linear with respect to the number of nodes in the power system. Training and test examples were generated by randomly sampling sets of power system measurements and annotated with the exact solutions of linear SE with PMUs. The numerical results demonstrate that the GNN model provides an accurate approximation of the SE solutions. Furthermore, errors caused by PMU malfunctions or communication failures that would normally make the SE problem unobservable have a local effect and do not deteriorate the results in the rest of the power system.
Upward Translation of Optimal and P-Optimal Proof Systems in the Boolean Hierarchy over NP
Venue: arXiv cs.CC 2023
Year: 2023

We study the existence of optimal and p-optimal proof systems for classes in the Boolean hierarchy over $\mathrm{NP}$. Our main results concern $\mathrm{DP}$, i.e., the second level of this hierarchy: If all sets in $\mathrm{DP}$ have p-optimal proof systems, then all sets in $\mathrm{coDP}$ have p-optimal proof systems. The analogous implication for optimal proof systems fails relative to an oracle. As a consequence, we clarify such implications for all classes $\mathcal{C}$ and $\mathcal{D}$ in the Boolean hierarchy over $\mathrm{NP}$: either we can prove the implication or show that it fails relative to an oracle. Furthermore, we show that the sets $\mathrm{SAT}$ and $\mathrm{TAUT}$ have p-optimal proof systems, if and only if all sets in the Boolean hierarchy over $\mathrm{NP}$ have p-optimal proof systems which is a new characterization of a conjecture studied by Pudlák.
Image-based Indian Sign Language Recognition: A Practical Review using Deep Neural Networks
Venue: arXiv cs.CV 2023
Year: 2023

People with vocal and hearing disabilities use sign language to express themselves using visual gestures and signs. Although sign language is a solution for communication difficulties faced by deaf people, there are still problems as most of the general population cannot understand this language, creating a communication barrier, especially in places such as banks, airports, supermarkets, etc. [1]. A sign language recognition(SLR) system is a must to solve this problem. The main focus of this model is to develop a real-time word-level sign language recognition system that would translate sign language to text. Much research has been done on ASL(American sign language). Thus, we have worked on ISL(Indian sign language) to cater to the needs of the deaf and hard-of-hearing community of India[2]. In this research, we provide an Indian Sign Language-based Sign Language recognition system. For this analysis, the user must be able to take pictures of hand movements using a web camera, and the system must anticipate and display the name of the taken picture. The acquired image goes through several processing phases, some of which use computer vision techniques, including grayscale conversion, dilatation, and masking. Our model is trained using a convolutional neural network (CNN), which is then utilized to recognize the images. Our best model has a 99% accuracy rate[3].
Polynomial time key-recovery attack on high rate random alternant codes
Venue: arXiv cs.IT 2023
Year: 2023

A long standing open question is whether the distinguisher of high rate alternant codes or Goppa codes \cite{FGOPT11} can be turned into an algorithm recovering the algebraic structure of such codes from the mere knowledge of an arbitrary generator matrix of it. This would allow to break the McEliece scheme as soon as the code rate is large enough and would break all instances of the CFS signature scheme. We give for the first time a positive answer for this problem when the code is {\em a generic alternant code} and when the code field size $q$ is small : $q \in \{2,3\}$ and for {\em all} regime of other parameters for which the aforementioned distinguisher works. This breakthrough has been obtained by two different ingredients : (i) a way of using code shortening and the component-wise product of codes to derive from the original alternant code a sequence of alternant codes of decreasing degree up to getting an alternant code of degree $3$ (with a multiplier and support related to those of the original alternant code); (ii) an original Gröbner basis approach which takes into account the non standard constraints on the multiplier and support of an alternant code which recovers in polynomial time the relevant algebraic structure of an alternant code of degree $3$ from the mere knowledge of a basis for it.
Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings?
Venue: arXiv cs.CL 2023
Year: 2023

Dense vector representations for textual data are crucial in modern NLP. Word embeddings and sentence embeddings estimated from raw texts are key in achieving state-of-the-art results in various tasks requiring semantic understanding. However, obtaining embeddings at the document level is challenging due to computational requirements and lack of appropriate data. Instead, most approaches fall back on computing document embeddings based on sentence representations. Although there exist architectures and models to encode documents fully, they are in general limited to English and few other high-resourced languages. In this work, we provide a systematic comparison of methods to produce document-level representations from sentences based on LASER, LaBSE, and Sentence BERT pre-trained multilingual models. We compare input token number truncation, sentence averaging as well as some simple windowing and in some cases new augmented and learnable approaches, on 3 multi- and cross-lingual tasks in 8 languages belonging to 3 different language families. Our task-based extrinsic evaluations show that, independently of the language, a clever combination of sentence embeddings is usually better than encoding the full document as a single unit, even when this is possible. We demonstrate that while a simple sentence average results in a strong baseline for classification tasks, more complex combinations are necessary for semantic tasks.
MCPrioQ: A lock-free algorithm for online sparse markov-chains
Venue: arXiv cs.LG 2023
Year: 2023

In high performance systems it is sometimes hard to build very large graphs that are efficient both with respect to memory and compute. This paper proposes a data structure called Markov-chain-priority-queue (MCPrioQ), which is a lock-free sparse markov-chain that enables online and continuous learning with time-complexity of $O(1)$ for updates and $O(CDF^{-1}(t))$ inference. MCPrioQ is especially suitable for recommender-systems for lookups of $n$-items in descending probability order. The concurrent updates are achieved using hash-tables and atomic instructions and the lookups are achieved through a novel priority-queue which allows for approximately correct results even during concurrent updates. The approximatly correct and lock-free property is maintained by a read-copy-update scheme, but where the semantics have been slightly updated to allow for swap of elements rather than the traditional pop-insert scheme.